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Source code for galaxy.model.store
import abc
import contextlib
import datetime
import os
import shutil
import tarfile
import tempfile
from collections import defaultdict
from json import (
dump,
dumps,
load,
)
from bdbag import bdbag_api as bdb
from boltons.iterutils import remap
from sqlalchemy.orm import joinedload
from sqlalchemy.sql import expression
from galaxy.exceptions import MalformedContents, ObjectNotFound
from galaxy.security.idencoding import IdEncodingHelper
from galaxy.util import FILENAME_VALID_CHARS
from galaxy.util import in_directory
from galaxy.util.bunch import Bunch
from galaxy.util.path import safe_walk
from ..custom_types import json_encoder
from ..item_attrs import add_item_annotation, get_item_annotation_str
from ... import model
ATTRS_FILENAME_HISTORY = 'history_attrs.txt'
ATTRS_FILENAME_DATASETS = 'datasets_attrs.txt'
ATTRS_FILENAME_JOBS = 'jobs_attrs.txt'
ATTRS_FILENAME_IMPLICIT_COLLECTION_JOBS = 'implicit_collection_jobs_attrs.txt'
ATTRS_FILENAME_COLLECTIONS = 'collections_attrs.txt'
ATTRS_FILENAME_EXPORT = 'export_attrs.txt'
ATTRS_FILENAME_LIBRARIES = 'libraries_attrs.txt'
GALAXY_EXPORT_VERSION = "2"
[docs]class ImportOptions:
[docs] def __init__(self, allow_edit=False, allow_library_creation=False, allow_dataset_object_edit=None):
self.allow_edit = allow_edit
self.allow_library_creation = allow_library_creation
if allow_dataset_object_edit is None:
allow_dataset_object_edit = allow_edit
self.allow_dataset_object_edit = allow_dataset_object_edit
[docs]class SessionlessContext:
[docs] def query(self, model_class):
def find(obj_id):
return self.objects.get(model_class, {}).get(obj_id) or None
return Bunch(find=find, get=find)
[docs]class ModelImportStore(metaclass=abc.ABCMeta):
[docs] def __init__(self, import_options=None, app=None, user=None, object_store=None, tag_handler=None):
if object_store is None:
if app is not None:
object_store = app.object_store
self.object_store = object_store
self.app = app
if app is not None:
self.sa_session = app.model.session
self.sessionless = False
else:
self.sa_session = SessionlessContext()
self.sessionless = True
self.user = user
self.import_options = import_options or ImportOptions()
self.dataset_state_serialized = True
self.tag_handler = tag_handler
[docs] @abc.abstractmethod
def defines_new_history(self):
"""Does this store define a new history to create."""
[docs] @abc.abstractmethod
def new_history_properties(self):
"""Dict of history properties if defines_new_history() is truthy."""
[docs] @abc.abstractmethod
def collections_properties(self):
"""Return a list of HDCA properties."""
@abc.abstractproperty
def object_key(self):
"""Key used to connect objects in metadata.
Legacy exports used 'hid' but associated objects may not be from the same history
and a history may contain multiple objects with the same 'hid'.
"""
[docs] @contextlib.contextmanager
def target_history(self, default_history=None):
new_history = None
if self.defines_new_history():
history_properties = self.new_history_properties()
history_name = history_properties.get('name')
if history_name:
history_name = f'imported from archive: {history_name}'
else:
history_name = 'unnamed imported history'
# Create history.
new_history = model.History(name=history_name,
user=self.user)
new_history.importing = True
hid_counter = history_properties.get('hid_counter')
genome_build = history_properties.get('genome_build')
# TODO: This seems like it shouldn't be imported, try to test and verify we can calculate this
# and get away without it. -John
if hid_counter:
new_history.hid_counter = hid_counter
if genome_build:
new_history.genome_build = genome_build
self._session_add(new_history)
self._flush()
if self.user:
add_item_annotation(self.sa_session, self.user, new_history, history_properties.get('annotation'))
history = new_history
else:
history = default_history
yield history
if new_history is not None:
# Done importing.
new_history.importing = False
self._flush()
[docs] def perform_import(self, history=None, new_history=False, job=None):
object_import_tracker = ObjectImportTracker()
datasets_attrs = self.datasets_properties()
collections_attrs = self.collections_properties()
self._import_datasets(object_import_tracker, datasets_attrs, history, new_history, job)
self._import_dataset_copied_associations(object_import_tracker, datasets_attrs)
self._import_libraries(object_import_tracker)
self._import_collection_instances(object_import_tracker, collections_attrs, history, new_history)
self._import_collection_implicit_input_associations(object_import_tracker, collections_attrs)
self._import_collection_copied_associations(object_import_tracker, collections_attrs)
self._reassign_hids(object_import_tracker, history)
self._import_jobs(object_import_tracker, history)
self._import_implicit_collection_jobs(object_import_tracker)
self._flush()
def _import_datasets(self, object_import_tracker, datasets_attrs, history, new_history, job):
object_key = self.object_key
for dataset_attrs in datasets_attrs:
if 'state' not in dataset_attrs:
self.dataset_state_serialized = False
def handle_dataset_object_edit(dataset_instance):
if "dataset" in dataset_attrs:
assert self.import_options.allow_dataset_object_edit
dataset_attributes = [
"state",
"deleted",
"purged",
"external_filename",
"_extra_files_path",
"file_size",
"object_store_id",
"total_size",
"created_from_basename",
"uuid",
]
for attribute in dataset_attributes:
if attribute in dataset_attrs["dataset"]:
setattr(dataset_instance.dataset, attribute, dataset_attrs["dataset"][attribute])
if "hashes" in dataset_attrs["dataset"]:
for hash_attrs in dataset_attrs["dataset"]["hashes"]:
hash_obj = model.DatasetHash()
hash_obj.hash_value = hash_attrs["hash_value"]
hash_obj.hash_function = hash_attrs["hash_function"]
hash_obj.extra_files_path = hash_attrs["extra_files_path"]
dataset_instance.dataset.hashes.append(hash_obj)
if 'id' in dataset_attrs["dataset"] and self.import_options.allow_edit:
dataset_instance.dataset.id = dataset_attrs["dataset"]['id']
if job:
dataset_instance.dataset.job_id = job.id
if 'id' in dataset_attrs and self.import_options.allow_edit and not self.sessionless:
dataset_instance = self.sa_session.query(getattr(model, dataset_attrs['model_class'])).get(dataset_attrs["id"])
attributes = [
"name",
"extension",
"info",
"blurb",
"peek",
"designation",
"visible",
"metadata",
"tool_version",
"validated_state",
"validated_state_message",
]
for attribute in attributes:
if attribute in dataset_attrs:
value = dataset_attrs.get(attribute)
if attribute == "metadata":
def remap_objects(p, k, obj):
if isinstance(obj, dict) and "model_class" in obj and obj["model_class"] == "MetadataFile":
return (k, model.MetadataFile(dataset=dataset_instance, uuid=obj["uuid"]))
return (k, obj)
value = remap(value, remap_objects)
setattr(dataset_instance, attribute, value)
handle_dataset_object_edit(dataset_instance)
else:
metadata = dataset_attrs['metadata']
model_class = dataset_attrs.get("model_class", "HistoryDatasetAssociation")
if model_class == "HistoryDatasetAssociation":
# Create dataset and HDA.
dataset_instance = model.HistoryDatasetAssociation(name=dataset_attrs['name'],
extension=dataset_attrs['extension'],
info=dataset_attrs['info'],
blurb=dataset_attrs['blurb'],
peek=dataset_attrs['peek'],
designation=dataset_attrs['designation'],
visible=dataset_attrs['visible'],
deleted=dataset_attrs.get('deleted', False),
dbkey=metadata['dbkey'],
tool_version=metadata.get('tool_version'),
metadata=metadata,
history=history,
create_dataset=True,
flush=False,
sa_session=self.sa_session)
elif model_class == "LibraryDatasetDatasetAssociation":
# Create dataset and LDDA.
dataset_instance = model.LibraryDatasetDatasetAssociation(name=dataset_attrs['name'],
extension=dataset_attrs['extension'],
info=dataset_attrs['info'],
blurb=dataset_attrs['blurb'],
peek=dataset_attrs['peek'],
designation=dataset_attrs['designation'],
visible=dataset_attrs['visible'],
deleted=dataset_attrs.get('deleted', False),
dbkey=metadata['dbkey'],
tool_version=metadata.get('tool_version'),
user=self.user,
metadata=metadata,
create_dataset=True,
flush=False,
sa_session=self.sa_session)
else:
raise Exception("Unknown dataset instance type encountered")
self._attach_raw_id_if_editing(dataset_instance, dataset_attrs)
# Older style...
if self.import_options.allow_edit:
if 'uuid' in dataset_attrs:
dataset_instance.dataset.uuid = dataset_attrs["uuid"]
if 'dataset_uuid' in dataset_attrs:
dataset_instance.dataset.uuid = dataset_attrs["dataset_uuid"]
self._session_add(dataset_instance)
if model_class == "HistoryDatasetAssociation":
# don't use add_history to manage HID handling across full import to try to preserve
# HID structure.
dataset_instance.history = history
if new_history and self.trust_hid(dataset_attrs):
dataset_instance.hid = dataset_attrs['hid']
else:
object_import_tracker.requires_hid.append(dataset_instance)
if 'dataset' in dataset_attrs:
handle_dataset_object_edit(dataset_instance)
else:
file_name = dataset_attrs.get('file_name')
if file_name:
# Do security check and move/copy dataset data.
archive_path = os.path.abspath(os.path.join(self.archive_dir, file_name))
if os.path.islink(archive_path):
raise MalformedContents(f"Invalid dataset path: {archive_path}")
temp_dataset_file_name = \
os.path.realpath(archive_path)
if not in_directory(temp_dataset_file_name, self.archive_dir):
raise MalformedContents(f"Invalid dataset path: {temp_dataset_file_name}")
if not file_name or not os.path.exists(temp_dataset_file_name):
dataset_instance.state = dataset_instance.states.DISCARDED
dataset_instance.deleted = True
dataset_instance.purged = True
dataset_instance.dataset.deleted = True
dataset_instance.dataset.purged = True
else:
dataset_instance.state = dataset_attrs.get('state', dataset_instance.states.OK)
self.object_store.update_from_file(dataset_instance.dataset, file_name=temp_dataset_file_name, create=True)
# Import additional files if present. Histories exported previously might not have this attribute set.
dataset_extra_files_path = dataset_attrs.get('extra_files_path', None)
if dataset_extra_files_path:
dir_name = dataset_instance.dataset.extra_files_path_name
dataset_extra_files_path = os.path.join(self.archive_dir, dataset_extra_files_path)
for root, _dirs, files in safe_walk(dataset_extra_files_path):
extra_dir = os.path.join(dir_name, root.replace(dataset_extra_files_path, '', 1).lstrip(os.path.sep))
extra_dir = os.path.normpath(extra_dir)
for extra_file in files:
source = os.path.join(root, extra_file)
if not in_directory(source, self.archive_dir):
raise MalformedContents(f"Invalid dataset path: {source}")
self.object_store.update_from_file(
dataset_instance.dataset, extra_dir=extra_dir,
alt_name=extra_file, file_name=source,
create=True)
dataset_instance.dataset.set_total_size() # update the filesize record in the database
if dataset_instance.deleted:
dataset_instance.dataset.deleted = True
if model_class == "HistoryDatasetAssociation" and self.user:
add_item_annotation(self.sa_session, self.user, dataset_instance, dataset_attrs['annotation'])
tag_list = dataset_attrs.get('tags')
if tag_list:
self.tag_handler.set_tags_from_list(user=self.user, item=dataset_instance, new_tags_list=tag_list, flush=False)
if self.app:
self.app.datatypes_registry.set_external_metadata_tool.regenerate_imported_metadata_if_needed(
dataset_instance, history, job
)
if model_class == "HistoryDatasetAssociation":
if object_key in dataset_attrs:
object_import_tracker.hdas_by_key[dataset_attrs[object_key]] = dataset_instance
else:
assert 'id' in dataset_attrs
object_import_tracker.hdas_by_id[dataset_attrs['id']] = dataset_instance
else:
if object_key in dataset_attrs:
object_import_tracker.lddas_by_key[dataset_attrs[object_key]] = dataset_instance
else:
assert 'id' in dataset_attrs
object_import_tracker.lddas_by_key[dataset_attrs['id']] = dataset_instance
def _import_libraries(self, object_import_tracker):
object_key = self.object_key
def import_folder(folder_attrs, root_folder=None):
if root_folder:
library_folder = root_folder
else:
name = folder_attrs['name']
description = folder_attrs['description']
genome_build = folder_attrs['genome_build']
deleted = folder_attrs['deleted']
library_folder = model.LibraryFolder(
name=name,
description=description,
genome_build=genome_build
)
library_folder.deleted = deleted
self._session_add(library_folder)
for sub_folder_attrs in folder_attrs.get("folders", []):
sub_folder = import_folder(sub_folder_attrs)
library_folder.add_folder(sub_folder)
for ld_attrs in folder_attrs.get("datasets", []):
ld = model.LibraryDataset(
folder=library_folder,
name=ld_attrs['name'],
info=ld_attrs['info'],
order_id=ld_attrs['order_id']
)
if 'ldda' in ld_attrs:
ldda = object_import_tracker.lddas_by_key[ld_attrs["ldda"][object_key]]
ld.library_dataset_dataset_association = ldda
self._session_add(ld)
self.sa_session.flush()
return library_folder
libraries_attrs = self.library_properties()
for library_attrs in libraries_attrs:
if library_attrs['model_class'] == 'LibraryFolder' and library_attrs.get('id') and not self.sessionless and self.import_options.allow_edit:
library_folder = self.sa_session.query(model.LibraryFolder).get(self.app.security.decode_id(library_attrs['id']))
import_folder(library_attrs, root_folder=library_folder)
else:
assert self.import_options.allow_library_creation
name = library_attrs['name']
description = library_attrs['description']
synopsis = library_attrs['synopsis']
library = model.Library(name=name, description=description, synopsis=synopsis)
self._session_add(library)
object_import_tracker.libraries_by_key[library_attrs[object_key]] = library
if 'root_folder' in library_attrs:
library.root_folder = import_folder(library_attrs['root_folder'])
def _import_collection_instances(self, object_import_tracker, collections_attrs, history, new_history):
object_key = self.object_key
def import_collection(collection_attrs):
def materialize_elements(dc):
if "elements" not in collection_attrs:
return
elements_attrs = collection_attrs['elements']
for element_attrs in elements_attrs:
dce = model.DatasetCollectionElement(collection=dc,
element=model.DatasetCollectionElement.UNINITIALIZED_ELEMENT,
element_index=element_attrs['element_index'],
element_identifier=element_attrs['element_identifier'])
if 'encoded_id' in element_attrs:
object_import_tracker.dces_by_key[element_attrs['encoded_id']] = dce
if 'hda' in element_attrs:
hda_attrs = element_attrs['hda']
if object_key in hda_attrs:
hda_key = hda_attrs[object_key]
hdas_by_key = object_import_tracker.hdas_by_key
if hda_key in hdas_by_key:
hda = hdas_by_key[hda_key]
else:
raise KeyError(f"Failed to find exported hda with key [{hda_key}] of type [{object_key}] in [{hdas_by_key}]")
else:
hda_id = hda_attrs["id"]
hdas_by_id = object_import_tracker.hdas_by_id
if hda_id not in hdas_by_id:
raise Exception(f"Failed to find HDA with id [{hda_id}] in [{hdas_by_id}]")
hda = hdas_by_id[hda_id]
dce.hda = hda
elif 'child_collection' in element_attrs:
dce.child_collection = import_collection(element_attrs['child_collection'])
else:
raise Exception("Unknown collection element type encountered.")
dc.element_count = len(elements_attrs)
if "id" in collection_attrs and self.import_options.allow_edit and not self.sessionless:
dc = self.sa_session.query(model.DatasetCollection).get(collection_attrs["id"])
attributes = [
"collection_type",
"populated_state",
"populated_state_message",
"element_count",
]
for attribute in attributes:
if attribute in collection_attrs:
setattr(dc, attribute, collection_attrs.get(attribute))
materialize_elements(dc)
else:
# create collection
dc = model.DatasetCollection(collection_type=collection_attrs['type'])
dc.populated_state = collection_attrs["populated_state"]
dc.populated_state_message = collection_attrs.get('populated_state_message')
self._attach_raw_id_if_editing(dc, collection_attrs)
materialize_elements(dc)
self._session_add(dc)
return dc
for collection_attrs in collections_attrs:
if 'collection' in collection_attrs:
dc = import_collection(collection_attrs["collection"])
if 'id' in collection_attrs and self.import_options.allow_edit and not self.sessionless:
hdca = self.sa_session.query(model.HistoryDatasetCollectionAssociation).get(collection_attrs["id"])
# TODO: edit attributes...
else:
hdca = model.HistoryDatasetCollectionAssociation(collection=dc,
visible=True,
name=collection_attrs['display_name'],
implicit_output_name=collection_attrs.get("implicit_output_name"))
self._attach_raw_id_if_editing(hdca, collection_attrs)
hdca.history = history
if new_history and self.trust_hid(collection_attrs):
hdca.hid = collection_attrs['hid']
else:
object_import_tracker.requires_hid.append(hdca)
self._session_add(hdca)
if object_key in collection_attrs:
object_import_tracker.hdcas_by_key[collection_attrs[object_key]] = hdca
else:
assert 'id' in collection_attrs
object_import_tracker.hdcas_by_id[collection_attrs['id']] = hdca
else:
import_collection(collection_attrs)
def _attach_raw_id_if_editing(self, obj, attrs):
if self.sessionless and 'id' in attrs and self.import_options.allow_edit:
obj.id = attrs['id']
def _import_collection_implicit_input_associations(self, object_import_tracker, collections_attrs):
object_key = self.object_key
for collection_attrs in collections_attrs:
if "id" in collection_attrs:
# Existing object, not a new one, this property is immutable via model stores currently.
continue
hdca = object_import_tracker.hdcas_by_key[collection_attrs[object_key]]
if "implicit_input_collections" in collection_attrs:
implicit_input_collections = collection_attrs["implicit_input_collections"]
for implicit_input_collection in implicit_input_collections:
name = implicit_input_collection["name"]
input_collection_identifier = implicit_input_collection["input_dataset_collection"]
if input_collection_identifier in object_import_tracker.hdcas_by_key:
input_dataset_collection = object_import_tracker.hdcas_by_key[input_collection_identifier]
hdca.add_implicit_input_collection(name, input_dataset_collection)
def _import_dataset_copied_associations(self, object_import_tracker, datasets_attrs):
object_key = self.object_key
# Re-establish copied_from_history_dataset_association relationships so history extraction
# has a greater chance of working in this history, for reproducibility.
for dataset_attrs in datasets_attrs:
if "id" in dataset_attrs:
# Existing object, not a new one, this property is not immutable via model stores currently.
continue
dataset_key = dataset_attrs[object_key]
if dataset_key not in object_import_tracker.hdas_by_key:
continue
hda = object_import_tracker.hdas_by_key[dataset_key]
copied_from_chain = dataset_attrs.get("copied_from_history_dataset_association_id_chain", [])
copied_from_object_key = _copied_from_object_key(copied_from_chain, object_import_tracker.hdas_by_key)
if not copied_from_object_key:
continue
# Re-establish the chain if we can.
if copied_from_object_key in object_import_tracker.hdas_by_key:
hda.copied_from_history_dataset_association = object_import_tracker.hdas_by_key[copied_from_object_key]
else:
# We're at the end of the chain and this HDA was copied from an HDA
# outside the history. So when we find this job and are looking for inputs/outputs
# attach to this node... unless we've already encountered another dataset
# copied from that jobs output... in that case we are going to cheat and
# say this dataset was copied from that one. It wasn't in the original Galaxy
# instance but I think it is fine to pretend in order to create a DAG here.
hda_copied_from_sinks = object_import_tracker.hda_copied_from_sinks
if copied_from_object_key in hda_copied_from_sinks:
hda.copied_from_history_dataset_association = object_import_tracker.hdas_by_key[hda_copied_from_sinks[copied_from_object_key]]
else:
hda_copied_from_sinks[copied_from_object_key] = dataset_key
def _import_collection_copied_associations(self, object_import_tracker, collections_attrs):
object_key = self.object_key
# Re-establish copied_from_history_dataset_collection_association relationships so history extraction
# has a greater chance of working in this history, for reproducibility. Very similar to HDA code above
# see comments there.
for collection_attrs in collections_attrs:
if "id" in collection_attrs:
# Existing object, not a new one, this property is immutable via model stores currently.
continue
dataset_collection_key = collection_attrs[object_key]
if dataset_collection_key not in object_import_tracker.hdcas_by_key:
continue
hdca = object_import_tracker.hdcas_by_key[dataset_collection_key]
copied_from_chain = collection_attrs.get("copied_from_history_dataset_collection_association_id_chain", [])
copied_from_object_key = _copied_from_object_key(copied_from_chain, object_import_tracker.hdcas_by_key)
if not copied_from_object_key:
continue
# Re-establish the chain if we can, again see comments for hdas above for this to make more
# sense.
hdca_copied_from_sinks = object_import_tracker.hdca_copied_from_sinks
if copied_from_object_key in object_import_tracker.hdcas_by_key:
hdca.copied_from_history_dataset_collection_association = object_import_tracker.hdcas_by_key[copied_from_object_key]
else:
if copied_from_object_key in hdca_copied_from_sinks:
hdca.copied_from_history_dataset_association = object_import_tracker.hdcas_by_key[hdca_copied_from_sinks[copied_from_object_key]]
else:
hdca_copied_from_sinks[copied_from_object_key] = dataset_collection_key
def _reassign_hids(self, object_import_tracker, history):
# assign HIDs for newly created objects that didn't match original history
requires_hid = object_import_tracker.requires_hid
requires_hid_len = len(requires_hid)
if requires_hid_len > 0 and not self.sessionless:
for obj in requires_hid:
history.stage_addition(obj)
history.add_pending_items()
def _import_jobs(self, object_import_tracker, history):
self._flush()
object_key = self.object_key
def _find_hda(input_key):
hda = None
if input_key in object_import_tracker.hdas_by_key:
hda = object_import_tracker.hdas_by_key[input_key]
if input_key in object_import_tracker.hda_copied_from_sinks:
hda = object_import_tracker.hdas_by_key[object_import_tracker.hda_copied_from_sinks[input_key]]
return hda
def _find_hdca(input_key):
hdca = None
if input_key in object_import_tracker.hdcas_by_key:
hdca = object_import_tracker.hdcas_by_key[input_key]
if input_key in object_import_tracker.hdca_copied_from_sinks:
hdca = object_import_tracker.hdcas_by_key[object_import_tracker.hdca_copied_from_sinks[input_key]]
return hdca
def _find_dce(input_key):
dce = None
if input_key in object_import_tracker.dces_by_key:
dce = object_import_tracker.dces_by_key[input_key]
return dce
#
# Create jobs.
#
jobs_attrs = self.jobs_properties()
# Create each job.
for job_attrs in jobs_attrs:
if 'id' in job_attrs:
# only thing we allow editing currently is associations for incoming jobs.
assert self.import_options.allow_edit
assert not self.sessionless
job = self.sa_session.query(model.Job).get(job_attrs["id"])
self._connect_job_io(job, job_attrs, _find_hda, _find_hdca, _find_dce)
continue
imported_job = model.Job()
imported_job.user = self.user
imported_job.history = history
imported_job.imported = True
imported_job.tool_id = job_attrs['tool_id']
imported_job.tool_version = job_attrs['tool_version']
raw_state = job_attrs['state']
if raw_state not in model.Job.terminal_states:
raw_state = model.Job.states.ERROR
imported_job.set_state(raw_state)
imported_job.info = job_attrs.get('info', None)
imported_job.exit_code = job_attrs.get('exit_code', None)
imported_job.traceback = job_attrs.get('traceback', None)
if 'stdout' in job_attrs:
# Pre 19.05 export.
imported_job.tool_stdout = job_attrs.get('stdout', None)
imported_job.tool_stderr = job_attrs.get('stderr', None)
else:
# Post 19.05 export with separated I/O
imported_job.tool_stdout = job_attrs.get('tool_stdout', None)
imported_job.job_stdout = job_attrs.get('job_stdout', None)
imported_job.tool_stderr = job_attrs.get('tool_stderr', None)
imported_job.job_stderr = job_attrs.get('job_stderr', None)
imported_job.command_line = job_attrs.get('command_line', None)
try:
imported_job.create_time = datetime.datetime.strptime(job_attrs["create_time"], "%Y-%m-%dT%H:%M:%S.%f")
imported_job.update_time = datetime.datetime.strptime(job_attrs["update_time"], "%Y-%m-%dT%H:%M:%S.%f")
except Exception:
pass
self._session_add(imported_job)
# Connect jobs to input and output datasets.
params = self._normalize_job_parameters(imported_job, job_attrs, _find_hda, _find_hdca, _find_dce)
for name, value in params.items():
# Transform parameter values when necessary.
imported_job.add_parameter(name, dumps(value))
self._connect_job_io(imported_job, job_attrs, _find_hda, _find_hdca, _find_dce)
if object_key in job_attrs:
object_import_tracker.jobs_by_key[job_attrs[object_key]] = imported_job
def _import_implicit_collection_jobs(self, object_import_tracker):
implicit_collection_jobs_attrs = self.implicit_collection_jobs_properties()
for icj_attrs in implicit_collection_jobs_attrs:
icj = model.ImplicitCollectionJobs()
icj.populated_state = icj_attrs["populated_state"]
icj.jobs = []
for order_index, job in enumerate(icj_attrs["jobs"]):
icja = model.ImplicitCollectionJobsJobAssociation()
icja.implicit_collection_jobs = icj
if job in object_import_tracker.jobs_by_key:
icja.job = object_import_tracker.jobs_by_key[job]
icja.order_index = order_index
icj.jobs.append(icja)
self._session_add(icja)
self._session_add(icj)
def _session_add(self, obj):
self.sa_session.add(obj)
def _flush(self):
self.sa_session.flush()
def _copied_from_object_key(copied_from_chain, objects_by_key):
if len(copied_from_chain) == 0:
return None
# Okay this gets fun, we need the last thing in the chain to reconnect jobs
# from outside the history to inputs/outputs in this history but there may
# be cycles in the chain that lead outside the original history, so just eliminate
# all IDs not from this history except the last one.
filtered_copied_from_chain = []
for i, copied_from_key in enumerate(copied_from_chain):
filter_id = (i != len(copied_from_chain) - 1) and (copied_from_key not in objects_by_key)
if not filter_id:
filtered_copied_from_chain.append(copied_from_key)
copied_from_chain = filtered_copied_from_chain
if len(copied_from_chain) == 0:
return None
copied_from_object_key = copied_from_chain[0]
return copied_from_object_key
[docs]class ObjectImportTracker:
"""Keep track of new and existing imported objects.
Needed to re-establish connections and such in multiple passes.
"""
[docs] def __init__(self):
self.libraries_by_key = {}
self.hdas_by_key = {}
self.hdas_by_id = {}
self.hdcas_by_key = {}
self.hdcas_by_id = {}
self.dces_by_key = {}
self.dces_by_id = {}
self.lddas_by_key = {}
self.hda_copied_from_sinks = {}
self.hdca_copied_from_sinks = {}
self.jobs_by_key = {}
self.requires_hid = []
[docs]def get_import_model_store_for_directory(archive_dir, **kwd):
if not os.path.isdir(archive_dir):
raise Exception(f"Could not find import model store for directory [{archive_dir}] (full path [{os.path.abspath(archive_dir)}])")
if os.path.exists(os.path.join(archive_dir, ATTRS_FILENAME_EXPORT)):
return DirectoryImportModelStoreLatest(archive_dir, **kwd)
else:
return DirectoryImportModelStore1901(archive_dir, **kwd)
[docs]class BaseDirectoryImportModelStore(ModelImportStore):
[docs] def defines_new_history(self):
new_history_attributes = os.path.join(self.archive_dir, ATTRS_FILENAME_HISTORY)
return os.path.exists(new_history_attributes)
[docs] def new_history_properties(self):
new_history_attributes = os.path.join(self.archive_dir, ATTRS_FILENAME_HISTORY)
history_properties = load(open(new_history_attributes))
return history_properties
[docs] def datasets_properties(self):
datasets_attrs_file_name = os.path.join(self.archive_dir, ATTRS_FILENAME_DATASETS)
datasets_attrs = load(open(datasets_attrs_file_name))
provenance_file_name = f"{datasets_attrs_file_name}.provenance"
if os.path.exists(provenance_file_name):
provenance_attrs = load(open(provenance_file_name))
datasets_attrs += provenance_attrs
return datasets_attrs
[docs] def collections_properties(self):
collections_attrs_file_name = os.path.join(self.archive_dir, ATTRS_FILENAME_COLLECTIONS)
if os.path.exists(collections_attrs_file_name):
collections_attrs = load(open(collections_attrs_file_name))
else:
collections_attrs = []
return collections_attrs
[docs] def library_properties(self):
libraries_attrs_file_name = os.path.join(self.archive_dir, ATTRS_FILENAME_LIBRARIES)
if os.path.exists(libraries_attrs_file_name):
libraries_attrs = load(open(libraries_attrs_file_name))
else:
libraries_attrs = []
return libraries_attrs
[docs] def jobs_properties(self):
jobs_attr_file_name = os.path.join(self.archive_dir, ATTRS_FILENAME_JOBS)
try:
return load(open(jobs_attr_file_name))
except FileNotFoundError:
return []
[docs] def implicit_collection_jobs_properties(self):
implicit_collection_jobs_attrs_file_name = os.path.join(self.archive_dir, ATTRS_FILENAME_IMPLICIT_COLLECTION_JOBS)
try:
return load(open(implicit_collection_jobs_attrs_file_name))
except FileNotFoundError:
return []
[docs]class DirectoryImportModelStore1901(BaseDirectoryImportModelStore):
object_key = 'hid'
[docs] def __init__(self, archive_dir, **kwd):
super().__init__(**kwd)
archive_dir = os.path.realpath(archive_dir)
# Bioblend previous to 17.01 exported histories with an extra subdir.
if not os.path.exists(os.path.join(archive_dir, ATTRS_FILENAME_HISTORY)):
for d in os.listdir(archive_dir):
if os.path.isdir(os.path.join(archive_dir, d)):
archive_dir = os.path.join(archive_dir, d)
break
self.archive_dir = archive_dir
def _connect_job_io(self, imported_job, job_attrs, _find_hda, _find_hdca, _find_dce):
for output_key in job_attrs['output_datasets']:
output_hda = _find_hda(output_key)
if output_hda:
if not self.dataset_state_serialized:
# dataset state has not been serialized, get state from job
output_hda.state = imported_job.state
imported_job.add_output_dataset(output_hda.name, output_hda)
if 'input_mapping' in job_attrs:
for input_name, input_key in job_attrs['input_mapping'].items():
input_hda = _find_hda(input_key)
if input_hda:
imported_job.add_input_dataset(input_name, input_hda)
def _normalize_job_parameters(self, imported_job, job_attrs, _find_hda, _find_hdca, _find_dce):
def remap_objects(p, k, obj):
if isinstance(obj, dict) and obj.get('__HistoryDatasetAssociation__', False):
imported_hda = _find_hda(obj[self.object_key])
if imported_hda:
return (k, {"src": "hda", "id": imported_hda.id})
return (k, obj)
params = job_attrs['params']
params = remap(params, remap_objects)
return params
[docs] def trust_hid(self, obj_attrs):
# We didn't do object tracking so we pretty much have to trust the HID and accept
# that it will be wrong a lot.
return True
[docs]class DirectoryImportModelStoreLatest(BaseDirectoryImportModelStore):
object_key = 'encoded_id'
[docs] def __init__(self, archive_dir, **kwd):
super().__init__(**kwd)
archive_dir = os.path.realpath(archive_dir)
self.archive_dir = archive_dir
if self.defines_new_history():
self.import_history_encoded_id = self.new_history_properties().get("encoded_id")
else:
self.import_history_encoded_id = None
def _connect_job_io(self, imported_job, job_attrs, _find_hda, _find_hdca, _find_dce):
if 'input_dataset_mapping' in job_attrs:
for input_name, input_keys in job_attrs['input_dataset_mapping'].items():
input_keys = input_keys or []
for input_key in input_keys:
input_hda = _find_hda(input_key)
if input_hda:
imported_job.add_input_dataset(input_name, input_hda)
if 'input_dataset_collection_mapping' in job_attrs:
for input_name, input_keys in job_attrs['input_dataset_collection_mapping'].items():
input_keys = input_keys or []
for input_key in input_keys:
input_hdca = _find_hdca(input_key)
if input_hdca:
imported_job.add_input_dataset_collection(input_name, input_hdca)
if 'input_dataset_collection_element_mapping' in job_attrs:
for input_name, input_keys in job_attrs['input_dataset_collection_element_mapping'].items():
input_keys = input_keys or []
for input_key in input_keys:
input_dce = _find_dce(input_key)
if input_dce:
imported_job.add_input_dataset_collection_element(input_name, input_dce)
if 'output_dataset_mapping' in job_attrs:
for output_name, output_keys in job_attrs['output_dataset_mapping'].items():
output_keys = output_keys or []
for output_key in output_keys:
output_hda = _find_hda(output_key)
if output_hda:
if not self.dataset_state_serialized:
# dataset state has not been serialized, get state from job
output_hda.state = imported_job.state
imported_job.add_output_dataset(output_name, output_hda)
if 'output_dataset_collection_mapping' in job_attrs:
for output_name, output_keys in job_attrs['output_dataset_collection_mapping'].items():
output_keys = output_keys or []
for output_key in output_keys:
output_hdca = _find_hdca(output_key)
if output_hdca:
imported_job.add_output_dataset_collection(output_name, output_hdca)
[docs] def trust_hid(self, obj_attrs):
return self.import_history_encoded_id and obj_attrs.get("history_encoded_id") == self.import_history_encoded_id
def _normalize_job_parameters(self, imported_job, job_attrs, _find_hda, _find_hdca, _find_dce):
def remap_objects(p, k, obj):
if isinstance(obj, dict) and "src" in obj and obj["src"] in ["hda", "hdca", "dce"]:
if obj["src"] == "hda":
imported_hda = _find_hda(obj["id"])
if imported_hda:
new_id = imported_hda.id
else:
new_id = None
elif obj["src"] == "hdca":
imported_hdca = _find_hdca(obj["id"])
if imported_hdca:
new_id = imported_hdca.id
else:
new_id = None
elif obj["src"] == "dce":
imported_dce = _find_dce(obj["id"])
if imported_dce:
new_id = imported_dce.id
else:
new_id = None
else:
raise NotImplementedError()
new_obj = obj.copy()
new_obj["id"] = new_id
return (k, new_obj)
return (k, obj)
params = job_attrs['params']
params = remap(params, remap_objects)
return params
[docs]class BagArchiveImportModelStore(DirectoryImportModelStoreLatest):
[docs] def __init__(self, bag_archive, **kwd):
archive_dir = tempfile.mkdtemp()
bdb.extract_bag(bag_archive, output_path=archive_dir)
# Why this line though...?
archive_dir = os.path.join(archive_dir, os.listdir(archive_dir)[0])
bdb.revert_bag(archive_dir)
super().__init__(archive_dir, **kwd)
[docs]class ModelExportStore(metaclass=abc.ABCMeta):
[docs] @abc.abstractmethod
def export_history(self, history, include_hidden=False, include_deleted=False):
"""Export history to store."""
[docs] @abc.abstractmethod
def add_dataset_collection(self, collection):
"""Add HDCA to export store."""
[docs] @abc.abstractmethod
def add_dataset(self, dataset, include_files=True):
"""Add HDA to export store."""
@abc.abstractmethod
def __enter__(self):
"""Export store should be used as context manager."""
@abc.abstractmethod
def __exit__(self, exc_type, exc_val, exc_tb):
"""Export store should be used as context manager."""
[docs]class DirectoryModelExportStore(ModelExportStore):
[docs] def __init__(self, export_directory, app=None, for_edit=False, serialize_dataset_objects=None, export_files=None, strip_metadata_files=True, serialize_jobs=True):
"""
:param export_directory: path to export directory. Will be created if it does not exist.
:param app: Galaxy App or app-like object. Must be provided if `for_edit` and/or `serialize_dataset_objects` are True
:param for_edit: Allow modifying existing HDA and dataset metadata during import.
:param serialize_dataset_objects: If True will encode IDs using the host secret. Defaults `for_edit`.
:param export_files: How files should be exported, can be 'symlink', 'copy' or None, in which case files
will not be serialized.
:param serialize_jobs: Include job data in model export. Not needed for set_metadata script.
"""
if not os.path.exists(export_directory):
os.makedirs(export_directory)
if app is not None:
self.app = app
security = app.security
sessionless = False
else:
sessionless = True
security = IdEncodingHelper(id_secret="randomdoesntmatter")
self.serialize_jobs = serialize_jobs
self.sessionless = sessionless
self.security = security
self.export_directory = export_directory
self.serialization_options = model.SerializationOptions(
for_edit=for_edit,
serialize_dataset_objects=serialize_dataset_objects,
strip_metadata_files=strip_metadata_files,
serialize_files_handler=self,
)
self.export_files = export_files
self.included_datasets = {}
self.included_collections = []
self.included_libraries = []
self.included_library_folders = []
self.collection_datasets = {}
self.collections_attrs = []
self.dataset_id_to_path = {}
self.job_output_dataset_associations = {}
[docs] def serialize_files(self, dataset, as_dict):
if self.export_files is None:
return None
elif self.export_files == "symlink":
add = os.symlink
elif self.export_files == "copy":
def add(src, dest):
if os.path.isdir(src):
shutil.copytree(src, dest)
else:
shutil.copyfile(src, dest)
export_directory = self.export_directory
_, include_files = self.included_datasets[dataset]
if not include_files:
return
file_name, extra_files_path = None, None
try:
_file_name = dataset.file_name
if os.path.exists(_file_name):
file_name = _file_name
except ObjectNotFound:
pass
if dataset.extra_files_path_exists():
extra_files_path = dataset.extra_files_path
else:
pass
dir_name = 'datasets'
dir_path = os.path.join(export_directory, dir_name)
dataset_hid = as_dict['hid']
assert dataset_hid, as_dict
if dataset.dataset.id in self.dataset_id_to_path:
file_name, extra_files_path = self.dataset_id_to_path[dataset.dataset.id]
if file_name is not None:
as_dict['file_name'] = file_name
if extra_files_path is not None:
as_dict['extra_files_path'] = extra_files_path
return
if file_name:
if not os.path.exists(dir_path):
os.makedirs(dir_path)
target_filename = get_export_dataset_filename(as_dict['name'], as_dict['extension'], dataset_hid)
arcname = os.path.join(dir_name, target_filename)
src = file_name
dest = os.path.join(export_directory, arcname)
add(src, dest)
as_dict['file_name'] = arcname
if extra_files_path:
try:
file_list = os.listdir(extra_files_path)
except OSError:
file_list = []
if len(file_list):
arcname = os.path.join(dir_name, f'extra_files_path_{dataset_hid}')
add(extra_files_path, os.path.join(export_directory, arcname))
as_dict['extra_files_path'] = arcname
else:
as_dict['extra_files_path'] = ''
self.dataset_id_to_path[dataset.dataset.id] = (as_dict.get("file_name"), as_dict.get("extra_files_path"))
[docs] def exported_key(self, obj):
return self.serialization_options.get_identifier(self.security, obj)
def __enter__(self):
return self
[docs] def export_history(self, history, include_hidden=False, include_deleted=False):
app = self.app
export_directory = self.export_directory
history_attrs = history.serialize(app.security, self.serialization_options)
history_attrs_filename = os.path.join(export_directory, ATTRS_FILENAME_HISTORY)
with open(history_attrs_filename, 'w') as history_attrs_out:
dump(history_attrs, history_attrs_out)
sa_session = app.model.session
# Write collections' attributes (including datasets list) to file.
query = (sa_session.query(model.HistoryDatasetCollectionAssociation)
.filter(model.HistoryDatasetCollectionAssociation.history == history)
.filter(model.HistoryDatasetCollectionAssociation.deleted == expression.false()))
collections = query.all()
for collection in collections:
# filter this ?
if not collection.populated:
break
if collection.state != 'ok':
break
self.add_dataset_collection(collection)
# export jobs for these datasets
for collection_dataset in collection.dataset_instances:
if collection_dataset.deleted and not include_deleted:
include_files = False
else:
include_files = True
self.add_dataset(collection_dataset, include_files=include_files)
self.collection_datasets[collection_dataset.id] = True
# Write datasets' attributes to file.
query = (sa_session.query(model.HistoryDatasetAssociation)
.filter(model.HistoryDatasetAssociation.history == history)
.join("dataset")
.options(joinedload("dataset").joinedload("actions"))
.order_by(model.HistoryDatasetAssociation.hid)
.filter(model.Dataset.purged == expression.false()))
datasets = query.all()
for dataset in datasets:
dataset.annotation = get_item_annotation_str(sa_session, history.user, dataset)
add_dataset = (dataset.visible or include_hidden) and (not dataset.deleted or include_deleted)
if dataset.id in self.collection_datasets:
add_dataset = True
if dataset not in self.included_datasets:
self.add_dataset(dataset, include_files=add_dataset)
[docs] def export_library(self, library, include_hidden=False, include_deleted=False):
self.included_libraries.append(library)
root_folder = getattr(library, 'root_folder', library)
self.included_library_folders.append(root_folder)
self.export_library_folder(root_folder, include_hidden=include_hidden, include_deleted=include_deleted)
[docs] def export_library_folder(self, library_folder, include_hidden=False, include_deleted=False):
for library_dataset in library_folder.datasets:
ldda = library_dataset.library_dataset_dataset_association
add_dataset = (not ldda.visible or not include_hidden) and (not ldda.deleted or include_deleted)
self.add_dataset(ldda, add_dataset)
for folder in library_folder.folders:
self.export_library_folder(folder, include_hidden=include_hidden, include_deleted=include_deleted)
[docs] def add_job_output_dataset_associations(self, job_id, name, dataset_instance):
job_output_dataset_associations = self.job_output_dataset_associations
if job_id not in job_output_dataset_associations:
job_output_dataset_associations[job_id] = {}
job_output_dataset_associations[job_id][name] = dataset_instance
[docs] def add_dataset_collection(self, collection):
self.collections_attrs.append(collection)
self.included_collections.append(collection)
[docs] def add_dataset(self, dataset, include_files=True):
self.included_datasets[dataset] = (dataset, include_files)
def _finalize(self):
export_directory = self.export_directory
datasets_attrs = []
provenance_attrs = []
for dataset, include_files in self.included_datasets.values():
if include_files:
datasets_attrs.append(dataset)
else:
provenance_attrs.append(dataset)
def to_json(attributes):
return json_encoder.encode([a.serialize(self.security, self.serialization_options) for a in attributes])
datasets_attrs_filename = os.path.join(export_directory, ATTRS_FILENAME_DATASETS)
with open(datasets_attrs_filename, 'w') as datasets_attrs_out:
datasets_attrs_out.write(to_json(datasets_attrs))
with open(f"{datasets_attrs_filename}.provenance", 'w') as provenance_attrs_out:
provenance_attrs_out.write(to_json(provenance_attrs))
libraries_attrs_filename = os.path.join(export_directory, ATTRS_FILENAME_LIBRARIES)
with open(libraries_attrs_filename, 'w') as libraries_attrs_out:
libraries_attrs_out.write(to_json(self.included_libraries))
collections_attrs_filename = os.path.join(export_directory, ATTRS_FILENAME_COLLECTIONS)
with open(collections_attrs_filename, 'w') as collections_attrs_out:
collections_attrs_out.write(to_json(self.collections_attrs))
jobs_attrs = []
for job_id, job_output_dataset_associations in self.job_output_dataset_associations.items():
output_dataset_mapping = {}
for name, dataset in job_output_dataset_associations.items():
if name not in output_dataset_mapping:
output_dataset_mapping[name] = []
output_dataset_mapping[name].append(self.exported_key(dataset))
jobs_attrs.append({"id": job_id, 'output_dataset_mapping': output_dataset_mapping})
if self.serialize_jobs:
#
# Write jobs attributes file.
#
# Get all jobs associated with included HDAs.
jobs_dict = {}
implicit_collection_jobs_dict = {}
def record_associated_jobs(obj):
# Get the job object.
job = None
for assoc in getattr(obj, 'creating_job_associations', []):
# For mapped over jobs obj could be DatasetCollection, which has no creating_job_association
job = assoc.job
break
if not job:
# No viable job.
return
jobs_dict[job.id] = job
icja = job.implicit_collection_jobs_association
if icja:
implicit_collection_jobs = icja.implicit_collection_jobs
implicit_collection_jobs_dict[implicit_collection_jobs.id] = implicit_collection_jobs
for hda, _include_files in self.included_datasets.values():
# Get the associated job, if any. If this hda was copied from another,
# we need to find the job that created the origial hda
job_hda = hda
while job_hda.copied_from_history_dataset_association: # should this check library datasets as well?
job_hda = job_hda.copied_from_history_dataset_association
if not job_hda.creating_job_associations:
# No viable HDA found.
continue
record_associated_jobs(job_hda)
for hdca in self.included_collections:
record_associated_jobs(hdca)
# Get jobs' attributes.
for job in jobs_dict.values():
if self.serialization_options.for_edit:
continue
job_attrs = job.serialize(self.security, self.serialization_options)
# -- Get input, output datasets. --
input_dataset_mapping = {}
output_dataset_mapping = {}
input_dataset_collection_mapping = {}
input_dataset_collection_element_mapping = {}
output_dataset_collection_mapping = {}
implicit_output_dataset_collection_mapping = {}
for assoc in job.input_datasets:
# Optional data inputs will not have a dataset.
if assoc.dataset:
name = assoc.name
if name not in input_dataset_mapping:
input_dataset_mapping[name] = []
input_dataset_mapping[name].append(self.exported_key(assoc.dataset))
for assoc in job.output_datasets:
# Optional data inputs will not have a dataset.
if assoc.dataset:
name = assoc.name
if name not in output_dataset_mapping:
output_dataset_mapping[name] = []
output_dataset_mapping[name].append(self.exported_key(assoc.dataset))
for assoc in job.input_dataset_collections:
# Optional data inputs will not have a dataset.
if assoc.dataset_collection:
name = assoc.name
if name not in input_dataset_collection_mapping:
input_dataset_collection_mapping[name] = []
input_dataset_collection_mapping[name].append(self.exported_key(assoc.dataset_collection))
for assoc in job.input_dataset_collection_elements:
if assoc.dataset_collection_element:
name = assoc.name
if name not in input_dataset_collection_element_mapping:
input_dataset_collection_element_mapping[name] = []
input_dataset_collection_element_mapping[name].append(self.exported_key(assoc.dataset_collection_element))
for assoc in job.output_dataset_collection_instances:
# Optional data outputs will not have a dataset.
if assoc.dataset_collection_instance:
name = assoc.name
if name not in output_dataset_collection_mapping:
output_dataset_collection_mapping[name] = []
output_dataset_collection_mapping[name].append(self.exported_key(assoc.dataset_collection_instance))
for assoc in job.output_dataset_collections:
if assoc.dataset_collection:
name = assoc.name
if name not in implicit_output_dataset_collection_mapping:
implicit_output_dataset_collection_mapping[name] = []
implicit_output_dataset_collection_mapping[name].append(self.exported_key(assoc.dataset_collection))
job_attrs['input_dataset_mapping'] = input_dataset_mapping
job_attrs['input_dataset_collection_mapping'] = input_dataset_collection_mapping
job_attrs['input_dataset_collection_element_mapping'] = input_dataset_collection_element_mapping
job_attrs['output_dataset_mapping'] = output_dataset_mapping
job_attrs['output_dataset_collection_mapping'] = output_dataset_collection_mapping
job_attrs['implicit_output_dataset_collection_mapping'] = implicit_output_dataset_collection_mapping
jobs_attrs.append(job_attrs)
icjs_attrs = []
for icj in implicit_collection_jobs_dict.values():
icj_attrs = icj.serialize(self.security, self.serialization_options)
icjs_attrs.append(icj_attrs)
icjs_attrs_filename = os.path.join(export_directory, ATTRS_FILENAME_IMPLICIT_COLLECTION_JOBS)
with open(icjs_attrs_filename, 'w') as icjs_attrs_out:
icjs_attrs_out.write(json_encoder.encode(icjs_attrs))
export_attrs_filename = os.path.join(export_directory, ATTRS_FILENAME_EXPORT)
with open(export_attrs_filename, 'w') as export_attrs_out:
dump({"galaxy_export_version": GALAXY_EXPORT_VERSION}, export_attrs_out)
jobs_attrs_filename = os.path.join(export_directory, ATTRS_FILENAME_JOBS)
with open(jobs_attrs_filename, 'w') as jobs_attrs_out:
jobs_attrs_out.write(json_encoder.encode(jobs_attrs))
def __exit__(self, exc_type, exc_val, exc_tb):
if exc_type is None:
self._finalize()
# http://effbot.org/zone/python-with-statement.htm
# Ignores TypeError exceptions
return isinstance(exc_val, TypeError)
[docs]class TarModelExportStore(DirectoryModelExportStore):
[docs] def __init__(self, out_file, gzip=True, **kwds):
self.gzip = gzip
self.out_file = out_file
temp_output_dir = tempfile.mkdtemp()
super().__init__(temp_output_dir, **kwds)
def _finalize(self):
super()._finalize()
tar_export_directory(self.export_directory, self.out_file, self.gzip)
shutil.rmtree(self.export_directory)
[docs]class BagDirectoryModelExportStore(DirectoryModelExportStore):
[docs] def __init__(self, out_directory, **kwds):
self.out_directory = out_directory
super().__init__(out_directory, **kwds)
def _finalize(self):
super()._finalize()
bdb.make_bag(self.out_directory)
[docs]class BagArchiveModelExportStore(BagDirectoryModelExportStore):
[docs] def __init__(self, out_file, bag_archiver="tgz", **kwds):
# bag_archiver in tgz, zip, tar
self.bag_archiver = bag_archiver
self.out_file = out_file
temp_output_dir = tempfile.mkdtemp()
super().__init__(temp_output_dir, **kwds)
def _finalize(self):
super()._finalize()
rval = bdb.archive_bag(self.export_directory, self.bag_archiver)
shutil.move(rval, self.out_file)
shutil.rmtree(self.export_directory)
[docs]def tar_export_directory(export_directory, out_file, gzip):
tarfile_mode = "w"
if gzip:
tarfile_mode += ":gz"
with tarfile.open(out_file, tarfile_mode, dereference=True) as history_archive:
for export_path in os.listdir(export_directory):
history_archive.add(os.path.join(export_directory, export_path), arcname=export_path)
[docs]def get_export_dataset_filename(name, ext, hid):
"""
Builds a filename for a dataset using its name an extension.
"""
base = ''.join(c in FILENAME_VALID_CHARS and c or '_' for c in name)
return f"{base}_{hid}.{ext}"
[docs]def imported_store_for_metadata(directory, object_store=None):
import_options = ImportOptions(allow_dataset_object_edit=True, allow_edit=True)
import_model_store = get_import_model_store_for_directory(directory, import_options=import_options, object_store=object_store)
import_model_store.perform_import()
return import_model_store